Archives: Agenda

• Evaluating study plans effectively to strengthen vendor selection and enhance data management strategies
• Aligning data management goals with vendor capabilities to reduce workload
• Enhancing vendor selection though effective identification methods

• Human governance of AI: principles, boundaries, and a human-in-the-loop model for clinical data decisions
• Responsible adoption: how to train, measure, and scale without losing traceability (ATR: Audit Trail Review, RBQM: Risk-Based Quality Management, quality, and ethics)
• Practical architecture: rules + ML + agentic AI for clinical data review—what to automate vs. what to keep human

• Overviewing current approaches and trends within clinical data management
• Assessing challenges and identifying solutions
• Understanding the where the industry is heading to curb competition

• Addressing key challenges small biotech’s face to minimize roadblocks in clinical data management
• Integrating strategies to ensure streamlined clinical data management processes
• Leveraging tools and innovation to ensure quality data collection that reduces study timelines

Consistent patient engagement is vital to clinical trial success, yet dropout and non-compliance remain persistent challenges. This session introduces gamification as a scientifically grounded strategy to boost retention, enhance adherence, and streamline trial execution.

• Understand the Science: Explore the behavioral and psychological foundations that make gamification a powerful tool for sustaining patient motivation
• See Real Results: Review real-world case studies where gamification significantly improved retention, compliance, and trial timelines
• Apply Proven Methods: Learn practical strategies and best practices for integrating gamified elements into clinical trial protocols

• Overviewing current AI trends to understand technical capabilities
• Identifying ways AI can improve data management practices to increase efficiency
• Incorporating AI effectively to minimize clinical trial timelines when managing data